Efficient similarity-based operations for data integration

Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results:In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions:Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

Section: Feature Extraction From Ontologiesmentioning

confidence: 99%

Annotation-Based Feature Extraction from Sets of SBML Models

Alm

Waltemath

Wolkenauer

et al. 2014

“…They have shown that searches with one or no error perform several times better than agrep, but as they do not apply any filtering techniques, agrep outperforms their implementation for larger k. Schallehn et al [15] describe a prefix trie based index for similarity search, joins and group operations for Oracle DB. The authors introduce operators, all based on depth-first traversal, for duplicate detection in heterogenous integration scenarios that outperform non-indexed similarity operators.…”

Section: Related Workmentioning

confidence: 99%

“…Applications arise in duplicate detection [15], error correction [13] and data cleansing [5], to name only a few. They are also of uttermost importance in the Life Sciences.…”

Section: Introductionmentioning

confidence: 99%

Prefix Tree Indexing for Similarity Search and Similarity Joins on Genomic Data

Rheinländer

Knobloch

Hochmuth

et al. 2010

Abstract. Similarity search and similarity join on strings are important for applications such as duplicate detection, error detection, data cleansing, or comparison of biological sequences. Especially DNA sequencing produces large collections of erroneous strings which need to be searched, compared, and merged. However, current RDBMS offer similarity operations only in a very limited and inefficient form that does not scale to the amount of data produced in Life Science projects. We present PETER, a prefix tree based indexing algorithm supporting approximate search and approimate joins. Our tool supports Hamming and edit distance as similarity measure and is available as C++ library, as Unix command line tool, and as cartridge for a commercial database. It combines an efficient implementation of compressed prefix trees with advanced pre-filtering techniques that exclude many candidate strings early. The achieved speed-ups are dramatic, especially for DNA with its small alphabet. We evaluate our tool on several collections of long strings containing up to 5,000,000 entries of length up to 3,500. We compare its performance to agrep, nrgrep, and user-defined functions inside a relational database. Our experiments reveal that PETER is faster by orders of magnitudes compared to the command-line tools. Compared to RDBMS, it computes similarity joins in minutes for which UDFs did not finish within a day and outperforms the built-in join methods even in the exact case.

“…More precisely, tool-links are mapped to a particular conditional join, the similarity join, in which data are joined if and only if they are very similar [17]. We considered several similarity functions based on those used by tools (Blast etc.).…”

Section: Towards a Meaning For Source-entities Pathsmentioning

confidence: 99%

A User-Centric Framework for Accessing Biological Sources and Tools

Cohen-Boulakia

Davidson

Froidevaux

2005

Biologists face two problems in interpreting their experiments: the integration of their data with information from multiple heterogeneous sources and data analysis with bioinformatics tools. It is difficult for scientists to choose between the numerous sources and tools without assistance. Following a thorough analysis of scientists' needs during the querying process, we found that biologists express preferences concerning the sources to be queried and the tools to be used. Interviews also showed that the querying process itself -the strategy followed -differs between scientists. In response to these findings, we have introduced a user-centric framework allowing to specify various querying processes. Then we have developed the BioGuide system which helps the scientists to choose suitable sources and tools, find complementary information in sources, and deal with divergent data. It is generic in that it can be adapted by each user to provide answers respecting his/her preferences, and obtained following his/her strategies. Abstract. Biologists face two problems in interpreting their experiments: the integration of their data with information from multiple heterogeneous sources and data analysis with bioinformatics tools. It is difficult for scientists to choose between the numerous sources and tools without assistance. Following a thorough analysis of scientists' needs during the querying process, we found that biologists express preferences concerning the sources to be queried and the tools to be used. Interviews also showed that the querying process itself -the strategy followed -differs between scientists. In response to these findings, we have introduced a user-centric framework allowing to specify various querying processes. Then we have developed the BioGuide system which helps the scientists to choose suitable sources and tools, find complementary information in sources, and deal with divergent data. It is generic in that it can be adapted by each user to provide answers respecting his/her preferences, and obtained following his/her strategies.